Update README.md

README.md

@@ -14,63 +14,29 @@ library_name: diffusers
     
 # controlnet- JFoz/dog-cat-pose
 
-These are controlnet weights trained on runwayml/stable-diffusion-v1-5 with pose conditioning generated using the animalpose model of OpenPifPaf 
-You can find some example images in the following. 
+Simple controlnet model made as part of the HF JaX/Diffusers community sprint.
+
+These are controlnet weights trained on runwayml/stable-diffusion-v1-5 with pose conditioning generated using the animalpose model of OpenPifPaf. 
+
+Some example images can be found in the following
 
 prompt: a tortoiseshell cat is sitting on a cushion
 ![images_0)](./images_0.png)
 prompt: a yellow dog standing on a lawn
 ![images_1)](./images_1.png)
 
+Whilst not the dataset used for this model, a smaller dataset with the same
+format for conditioning images can be found at https://huggingface.co/datasets/JFoz/dog-poses-controlnet-dataset
+
+The dataset was generated using the code at https://github.com/jfozard/animalpose/tree/f1be80ed29886a1314054b87f2a8944ea98997ac
 
 
 # Model Card for dog-cat-pose
 
-<!-- Provide a quick summary of what the model is/does. [Optional] -->
 This is an ControlNet model which allows users to control the pose of a dog or cat. Poses were extracted from images using the animalpose model of OpenPifPaf https://openpifpaf.github.io/intro.html . Skeleton colouring is as shown in the dataset. See also https://huggingface.co/JFoz/dog-pose
 
 
 
-
-#  Table of Contents
-
-- [Model Card for dog-cat-pose](#model-card-for--model_id-)
-- [Table of Contents](#table-of-contents)
-- [Table of Contents](#table-of-contents-1)
-- [Model Details](#model-details)
-  - [Model Description](#model-description)
-- [Uses](#uses)
-  - [Direct Use](#direct-use)
-  - [Downstream Use [Optional]](#downstream-use-optional)
-  - [Out-of-Scope Use](#out-of-scope-use)
-- [Bias, Risks, and Limitations](#bias-risks-and-limitations)
-  - [Recommendations](#recommendations)
-- [Training Details](#training-details)
-  - [Training Data](#training-data)
-  - [Training Procedure](#training-procedure)
-    - [Preprocessing](#preprocessing)
-    - [Speeds, Sizes, Times](#speeds-sizes-times)
-- [Evaluation](#evaluation)
-  - [Testing Data, Factors & Metrics](#testing-data-factors--metrics)
-    - [Testing Data](#testing-data)
-    - [Factors](#factors)
-    - [Metrics](#metrics)
-  - [Results](#results)
-- [Model Examination](#model-examination)
-- [Environmental Impact](#environmental-impact)
-- [Technical Specifications [optional]](#technical-specifications-optional)
-  - [Model Architecture and Objective](#model-architecture-and-objective)
-  - [Compute Infrastructure](#compute-infrastructure)
-    - [Hardware](#hardware)
-    - [Software](#software)
-- [Citation](#citation)
-- [Glossary [optional]](#glossary-optional)
-- [More Information [optional]](#more-information-optional)
-- [Model Card Authors [optional]](#model-card-authors-optional)
-- [Model Card Contact](#model-card-contact)
-- [How to Get Started with the Model](#how-to-get-started-with-the-model)
-
-
 # Model Details
 
 ## Model Description
@@ -83,7 +49,7 @@ This is an ControlNet model which allows users to control the pose of a dog or c
 - **Language(s) (NLP):** en
 - **License:** openrail
 - **Parent Model:** https://huggingface.co/runwayml/stable-diffusion-v1-5
-- **Resources for more information:** More information needed
+- **Resources for more information:** 
     - [GitHub Repo](https://github.com/jfozard/animalpose/tree/f1be80ed29886a1314054b87f2a8944ea98997ac)
 
 
@@ -99,7 +65,6 @@ This is an ControlNet model which allows users to control the pose of a dog or c
 Supply a suitable, potentially incomplete pose along with  a relevant text prompt
 
 
-
 ## Out-of-Scope Use
 
 <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
@@ -112,7 +77,7 @@ Generating images of non-animals. We advise retaining the stable diffusion safet
 
 <!-- This section is meant to convey both technical and sociotechnical limitations. -->
 
-
+The model is trained on a relatively small dataset, and may be overfit to those images.
 
 
 ## Recommendations
@@ -131,7 +96,6 @@ Maintain careful supervision of model inputs and outputs.
 
 Trained on a subset of Laion-5B using clip retrieval with the prompts &#34;a photo of a (dog/cat) (standing/walking)&#34;
 
-
 ## Training Procedure
 
 <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
@@ -140,96 +104,20 @@ Trained on a subset of Laion-5B using clip retrieval with the prompts &#34;a pho
 
 Images were rescaled to 512 along their short edge and centrally cropped. The OpenPifPaf pose-detection model was used to extract poses, which were used to generate conditioning images.
 
-### Speeds, Sizes, Times
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-More information needed
- 
-# Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-## Testing Data, Factors & Metrics
-
-### Testing Data
-
-<!-- This should link to a Data Card if possible. -->
-
-More information needed
-
-
-### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-More information needed
-
-### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-More information needed
-
-## Results 
 
-More information needed
 
-# Model Examination
-
-More information needed
-
-# Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** More information needed
-- **Hours used:** More information needed
-- **Cloud Provider:** More information needed
-- **Compute Region:** More information needed
-- **Carbon Emitted:** More information needed
-
-# Technical Specifications [optional]
-
-## Model Architecture and Objective
-
-More information needed
 
 ## Compute Infrastructure
 
 TPUv4i
 
-### Hardware
 
-More information needed
 
 ### Software
 
 Flax stable diffusion controlnet pipeline
 
-# Citation
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-More information needed
-
-**APA:**
 
-More information needed
-
-# Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-More information needed
-
-# More Information [optional]
-
-More information needed
 
 # Model Card Authors [optional]
 
@@ -237,19 +125,3 @@ More information needed
 
 John Fozard
 
-# Model Card Contact
-
-More information needed
-
-# How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-<details>
-<summary> Click to expand </summary>
-
-from diffusers import DiffusionPipeline
-
-pipeline = DiffusionPipeline.from_pretrained(&#34;dog-cat-pose&#34;${model.private ? &#34;, use_auth_token=True&#34; : &#34;&#34;})
-
-</details>